Towards an Automated Multimodal Approach for Video Summarization: Building a Bridge Between Text, Audio and Facial Cue-Based Summarization

2025-06-30

AI Summary

To address the pressing need for multimodal temporal alignment and behavioral understanding in video summarization for educational, professional, and social applications, this paper proposes a behavior-aware multimodal video summarization framework. Methodologically, it fuses prosodic features from speech, textual keywords, facial expressions, and LLM-generated pseudo-labels; introduces a “reward word” mechanism to identify cross-modal salient semantic units; and performs timestamp-level important segment detection via multimodal alignment and collaborative modeling. The key contribution lies in explicitly incorporating behavioral cues—such as emotional expression and verbal emphasis—into the summarization process, overcoming limitations of conventional unimodal or static multimodal approaches. Experiments demonstrate significant improvements: ROUGE-1 of 0.7929, BERTScore of 0.9536, and a 23% gain in video-level F1-score over baselines including Edmundson.

Technology Category

• Natural Language Processing: Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The increasing volume of video content in educational, professional, and social domains necessitates effective summarization techniques that go beyond traditional unimodal approaches. This paper proposes a behaviour-aware multimodal video summarization framework that integrates textual, audio, and visual cues to generate timestamp-aligned summaries. By extracting prosodic features, textual cues and visual indicators, the framework identifies semantically and emotionally important moments. A key contribution is the identification of bonus words, which are terms emphasized across multiple modalities and used to improve the semantic relevance and expressive clarity of the summaries. The approach is evaluated against pseudo-ground truth (pGT) summaries generated using LLM-based extractive method. Experimental results demonstrate significant improvements over traditional extractive method, such as the Edmundson method, in both text and video-based evaluation metrics. Text-based metrics show ROUGE-1 increasing from 0.4769 to 0.7929 and BERTScore from 0.9152 to 0.9536, while in video-based evaluation, our proposed framework improves F1-Score by almost 23%. The findings underscore the potential of multimodal integration in producing comprehensive and behaviourally informed video summaries.

Problem

• Integrating text, audio, and visual cues for video summarization
• Identifying bonus words to enhance summary relevance and clarity
• Improving performance over traditional extractive summarization methods

Innovation

• Multimodal framework integrates text, audio, visual cues
• Identifies bonus words across modalities for clarity
• Improves ROUGE-1 and BERTScore significantly